The field of the invention is object recognition trait analysis technologies.
The following background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Image-based object recognition technologies rely heavily on recognition algorithms that extract features from images. For example, U.S. Pat. No. 6,711,293 to Lowe titled “Method and Apparatus for Identifying Scale Invariant Features in an Image and Use of Same for Locating an Object in an Image”, filed Mar. 6, 2000, describes an algorithm, Scale Invariant Feature Transform (SIFT), that generates scale invariant features in the form of descriptors. Unfortunately, the invariance of the descriptors can lead to problems. If an object or set of objects comprises very similar or repetitious features, then the generated descriptors will be very similar to each other thereby reducing their object discriminating capabilities. Further, in image data representative of complex scenes or environments having many objects, such algorithms could result in thousands of descriptors, which could include many false positive matches to known objects.
Others have put forth effort to identify how to discriminate objects based on a variance within the image data itself. For example, U.S. Pat. No. 8,306,257 to Shiell et al. titled “Hierarchical Tree AAM”, filed Jan. 31, 2011, leverages a variance measure with respect to a model image to determine how to divide sets of images among nodes of a tree where the variance of images is with respect to an image model. Further progress is made by U.S. Pat. No. 6,894,639 to Katz titled “Generalized Hebbian Learning for Principal Component Analysis and Automatic Target Recognition, Systems and Methods”, filed Dec. 18, 1991. Katz describes using selected target specific feature information to distinguish targets from background clutter in image data. Katz leverages variances in image features via principle component filters to separate targets. Interestingly, Katz only focuses on deriving variances with respect to data available only from image data. Still, such principle component filters would fail in a case where there are a large number of objects having very similar features; bricks in a wall for example.
Additional progress has been made by combining gravity information with features as discussed in Kurz et al. titled “Gravity-Aware Handheld Augmented Reality” (Proc. IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR2011), pp. 111-120, Basel, Switzerland, 2011) and in Kurz et al. titled “Inertial sensor-aligned visual feature descriptors” (Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR2011), pp. 161-166, Colorado Springs, USA, 2011.). Using gravity information provides, at least at some level, an ability to distinguish similar descriptors; descriptors generated from corners of a window for example
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